提出C²MIL模型，提升病理图像生存分析的鲁棒性与可解释性。
C$^2$MIL: Synchronizing Semantic and Topological Causalities in Multiple Instance Learning for Robust and Interpretable Survival Analysis
- 构建双因果图模型，分离语义与拓扑因果关系。
- 在多个数据集上显著提升生存预测准确率与模型可解释性。
- 适合关注病理图像分析可解释性的研究人员使用。
基于图的多实例学习（MIL）广泛应用于以苏木精-伊红（H&E）染色的全切片图像（WSIs）进行生存分析，因其能捕捉拓扑信息。然而，染色和扫描差异会引入语义偏差，无关的拓扑子图会产生噪声，导致滑动级别表示的偏差，影响分析的可解释性与泛化能力。为此，我们以双重结构因果模型为理论基础，提出一种新型且可解释的双因果图基MIL模型C²MIL。该模型包含新颖的跨尺度自适应特征解耦模块用于语义因果干预，以及新的伯努利可微因果子图采样方法实现拓扑因果发现。结合解耦监督与对比学习的联合优化策略，同步优化语义与拓扑因果性。实验表明，C²MIL在多个基准上持续提升泛化性能与可解释性，可作为多种MIL基线的因果增强工具。代码已开源：https://github.com/mimic0127/C2MIL。
原文摘要 · Abstract (English)
Graph-based Multiple Instance Learning (MIL) is widely used in survival analysis with Hematoxylin and Eosin (H\&E)-stained whole slide images (WSIs) due to its ability to capture topological information. However, variations in staining and scanning can introduce semantic bias, while topological subgraphs that are not relevant to the causal relationships can create noise, resulting in biased slide-level representations. These issues can hinder both the interpretability and generalization of the analysis. To tackle this, we introduce a dual structural causal model as the theoretical foundation and propose a novel and interpretable dual causal graph-based MIL model, C$^2$MIL. C$^2$MIL incorporates a novel cross-scale adaptive feature disentangling module for semantic causal intervention and a new Bernoulli differentiable causal subgraph sampling method for topological causal discovery. A joint optimization strategy combining disentangling supervision and contrastive learning enables simultaneous refinement of both semantic and topological causalities. Experiments demonstrate that C$^2$MIL consistently improves generalization and interpretability over existing methods and can serve as a causal enhancement for diverse MIL baselines. The code is available at https://github.com/mimic0127/C2MIL.
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